Object Detection by YOLO

Object Detection

Object Detection is a computer vision task that involves identifying and locating objects of interest within an image or a video. The main objectives are to identify objects, ascertain their classes, and supply bounding box coordinates surrounding them.

YoloV3

YOLOv3 is an object detection technique that predicts bounding boxes and class probabilities for each grid cell by first dividing the input image into a grid. YOLO is effective for real-time applications since it processes the entire image in a single forward pass, in contrast to typical object recognition techniques that rely on region proposal networks and intricate pipelines.

Prerequisites:

pip install opencv-python
pip install tensorflow

You Only Look Once (YOLO) is an extremely fast and accurate, real-time, state-of-the-art object-detecting technology. In this article, using Tensorflow 2.0, we will implement YOLO from scratch.

A key component of computer vision is object detection, and our implementation uses TensorFlow to create the cutting-edge object detection model YOLOv3.